Updating Salamander Datasets with Phenotypic and Stomach Content

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OPEN Updating salamander datasets with Data Descriptor phenotypic and stomach content information for two mainland Speleomantes Enrico Lunghi 1,2,3,4 ✉ , Fabio Cianferoni 2,5, Simone Giachello6, Yahui Zhao 1 ✉ , Raoul Manenti 6,7, Claudia Corti2 & Gentile Francesco Ficetola 6,8

European plethodontid salamanders (genus Speleomantes; formerly Hydromantes) are a group of eight strictly protected amphibian species which are sensitive to human-induced environmental changes. Long-term monitoring is highly recommended to evaluate their status and to assess potential threats. Here we used two low-impact methodologies to build up a large dataset on two mainland Speleomantes species (S. strinatii and S. ambrosii), which represents an update to two previously published datasets, but also includes several new populations. Specifcally, we provide a set of 851 high quality images and a table gathering stomach contents recognized from 560 salamanders. This dataset ofers the opportunity to analyse phenotypic traits and stomach contents of eight populations belonging to two Speleomantes species. Furthermore, the data collection performed over diferent periods allows to expand the potential analyses through a wide temporal scale, allowing long-term studies.

Background & Summary European cave salamanders are a group of eight amphibians endemic to Italy and to a small part of the French Provence1, all belonging to the genus Speleomantes1 (formerly considered Hydromantes). Tree species (S. strinatii, S. ambrosii and S. italicus) are distributed along the Northern and Central Apennines (only S. strinatii naturally extends its range also in France), while the other fve (S. favus, S. supramontis, S. imperialis, S. sarrabusensis and S. genei) are endemic to Sardinia1. European cave salamanders are fully terrestrial and lack lungs (Lanza et al., 2006). Tese features force Speleomantes to select only specifc microclimatic conditions: they need high moisture and relatively cold temperatures to survive2. Terefore, Speleomantes ofen inhabit subterranean envi- ronments3,4, where their preferred microclimatic conditions are realized5. Nonetheless, subterranean environments also may be chosen by Speleomantes because predator pressure is lower if compared to epigean ones6,7. Indeed, Speleomantes likely represent one of the apex predators in these environments8, preying on a wide array of taxa9. Speleomantes’ narrow eco-physiological requirements, combined with their limited distributions and high site fdelity1,10, make these species very sensitive to human-induced efects and susceptible to extinction11,12; all Speleomantes species are therefore strictly protected13,14. Improving our knowledge of species at risk of extinction is fundamental to assess their potential threats and to guarantee their survival15,16. For example, prolonged monitoring may help to understand the impact of specifc environmental changes17–19, allowing to forecast future scenarios and promptly act to protect endangered spe- cies20–22. In this regard, the production of comparable datasets through time is of key importance9,23,24. However, data collection may not always be an easy task. Species can occur in habitats that pose challenges to human

1Key Laboratory of the Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beichen West Road 1, 100101, Beijing, China. 2Museo di Storia Naturale, Università degli Studi di Firenze, Via Romana 17, 50125, Firenze, Italy. 3Natural Oasis, via di Galceti 141, 59100, Prato, Italy. 4Unione Speleologica Calenzano, Piazza della Stazione 1, 50041Calenzano, Firenze, Italy. 5Istituto di Ricerca sugli Ecosistemi Terrestri (IRET), Consiglio Nazionale delle Ricerche (CNR), Via Madonna del Piano, 50019 Sesto Fiorentino, Firenze, Italy.6 Dipartimento di Scienze e Politiche Ambientali, Università degli Studi di Milano, via Celoria 26, 20133, Milano, Italy. 7Laboratorio di Biologia Sotterranea “Enrico Pezzoli”, Parco Regionale del Monte Barro, 23851, Galbiate, Italy. 8Université Grenoble Alpes, CNRS, Laboratoire d’Écologie Alpine (LECA), CS 40700, 38058, Grenoble, France. ✉e-mail: [email protected]; [email protected]

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Fig. 1 Map of the surveyed populations. Yellow labels indicate the surveyed sites for Speleomantes strinatii, while red ones those surveyed for S. ambrosii. Stars indicate the artifcial subterranean environments, while circles identify natural ones. Precise coordinates are not shown to increase populations protection58.

exploration, as for subterranean environments, where sampling and progression require considerable efort and specifc technical skills25–27. Subterranean habitats are not only hard to fnd or explore, but their peculiar environmental conditions (e.g., cold temperatures, high moisture, narrow space) might challenge surveyor’s stamina with a negative impact on the data recorded25,28. Another limit to data collection can be determined by the techniques used to collect information. For example, the old-fashioned research methods involving the sacrifce/harm of individuals are now widely condemned and avoided, especially when concerning protected species29,30. Terefore, there is an increasing trend in the use of new harmless alternative approaches31,32. We here describe a new database reporting data on two endangered Speleomantes species that can be handled only under specifc national authorizations (see Acknowledgments). Tis dataset includes information on the population structure, phenotypic traits and diet of individuals belonging to two mainland Speleomantes species: S. strinatii and S. ambrosii. Te information gathered here was collected adopting methodologies that limit negative impact on individuals, and can be combined with the two previously published datasets on these species9,23, extending the available information in space and time. In this work we gathered data from new populations to cover more area of the species’ range, but we also repeated the surveys in previously visited populations, thus providing temporal series of information allowing long-term studies focusing on populations but also on single individuals as well33,34. Specifcally, we here describe a dataset composed of two types of data: images and stomach contents. High quality images allow extrapolation of data on multiple phenotypic traits (e.g., size, morphology, coloration), obtaining information on the overall population traits and on single individuals as well23,33,35. In the era of digitization36,37, this new “living” digital catalogue (i.e., collection composed by photos of living organisms) will partially replace the natural history museum collections, becoming, at least for some animal groups, an alternative that not only spare animals lives, but also overcome classic limits such as space needed to store specimens and readiness to be used by the worldwide scientifc community with no costs37. Another advantage of “living” collections is its repeatability, namely the possibility that individuals can be digitally collected multiple times, allowing to perform long term studies on single individuals and populations as well. Te data related to stomach contents can be analysed to study the species’ trophic niche and the multiple related traits34,38,39. Nonetheless, comparing datasets produced over diferent time allows to assess potential variation afecting specifc species traits and infer on the possible causes40,41. Methods Surveyed sites. We surveyed eight subterranean sites, three artifcial mines and fve natural caves (Fig. 1); all these sites fall within the species’ natural range1. Surveys were performed in 2020, between 9 am and 6 pm during warm and sunny days, periods in which subterranean abundance of Speleomantes is the highest42. All sites were surveyed in July, while for six of them surveys were also repeated in September (Table 1). We performed exten- sive research of Speleomantes within the subterranean sites43, covering areas where the exploration was possible without speleological equipment. Salamanders were captured and placed in drilled plastic boxes waiting to be checked. When salamanders sampling was fnished, we proceeded with the data collection following this order: (i) assessment of the presence/absence of the mental gland for the identifcation of adult males (see Fig. 1a in35); (ii) record of body weight using a digital scale (precision 0.01 g); (iii) stomach fushing (see below); (iv) photo shooting (see below). Salamanders were then released in their collection points.

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Column Data description Typology of data 1 ID Te salamander’s database code 2 Site “Cave” or “Mine” 3–4 Latitude and Longitude Low resolution coordinates of the site 5 Population Te population code 6–7 Region and Province Te relevant information for each site 8–9 Month and Year Te period in which the salamander was captured 10 Species Te species to which the individual belongs 11 N_photo Te unique fle number corresponding to each individual 12 Weight Indicates the salamander’s weight (g) 13 Mental_gland Indicates the presence (1) or absence (0) of the male mental gland 14 Eggs Indicates whether eggs where visible through the salamander’s belly (yes/no) 15 Scale_bar Indicates the size of the picture scale bar (mm) 16 Condition Indicates if stomach was empty (1) or not (0) 17 Not_identifable Indicates if stomach content was identifable (0) or not (1) 18 to 34 Prey typology For each prey typology the total number of recognized items is reported

Table 1. Updating photos and stomach contents datasets for two mainland Speleomantes salamanders: data from summer 2020. Detailed information related to the photographed salamanders and to their stomach contents45.

Stomach fushing. Stomach fushing is a technique enabling to inspect amphibians stomach contents without harming individuals29,44. A detailed description of this methodology is provided in Lunghi, et al.9. Preserved stomach contents were examined in the lab using an optical microscope, and undigested prey items (or part of them; see9) were recognized at the order level; Staphylinidae (Coleoptera) and Formicidae (Hymenoptera) insect families were considered separately. When possible, also arthropods’ different stages where considered as independent prey category. Stomach contents were considered: “empty”, if no prey item was observed; “not-identifable”, if the advanced stage of digestion prevented the identifcation to the order level; “full”, if at least one prey item was recognizable. Recognized prey were counted following the method described in Lunghi, et al.9. Prey items were rarely integer and the identifcation was ofen based on fragments. Tis condition hampered any potential standardized measurement of prey volume using representative geometric polygons. Stomach fushing was performed on a subsample of the captured salamanders (Table 1).

Photo shooting. In a dark area of the subterranean environment, captured salamanders were dorsally photographed inside a white sof-box to obtain an homogeneous illumination of the subject and reduce shadows23. Before each photo session, a photo was shot to a Pantone colour card X-Rite Colorcheker Passport 2 placed into the sof-box to correctly calibrate the colours and light of photos during post-production23. We then photographed salamanders next to a plastic ruler to have a standardize size reference. Please refer to23 for additional information on this method. Data Records Te dataset (Photos and stomach contents of two mainland Italian Speleomantes salamanders: data from summer 202045) can be downloaded from fgshare and consists of:

1. 854 photos of salamanders from two mainland Speleomantes species: S. strinatii (S_strinatii4, 28 in July; S_strinatii1, 47 in July and 73 in September; S_strinatii2, 15 in July and 10 in September; S_strinatii5, 118 in July and 88 in September, S_strinatii6, 26 in July) and S. ambrosii (S_ambrosii2, 108 in July and 98 in September; S_ambrosii3, 21 in July and 51 in September; S_ambrosii4, 59 in July and 112 in September). Te code adopted to label populations (name of the species + number indicating the population) enables to combine the present dataset with two previously published (see Usage Notes). 2. 565 salamanders stomach contents, subdivided in 313 individuals with empty stomach, 37 with non-recognizable contents, and 215 individuals with full stomach. 3. 978 recognized prey items belonging to 17 diferent prey categories (Pulmonata, Araneae, Pseudoscorpi- ones, Opiliones, Julida, Isopoda, Entomobryomorpha, Orthoptera, Blattodea, Hymenoptera, Formicidae, Coleoptera, Diptera, Diptera_larva, Archaeognatha, Speleomantes_skin, Haplotaxida). 4. NA means no specifc data existing.

Detailed explanation of dataset Photos and stomach contents of two mainland Italian Speleomantes salamanders: data from summer 202045 is given in Table 1. Technical Validation Tis dataset provides data on two strictly protected amphibian species13. Salamanders were sampled following protocols aiming to avoid the spread of potential pathogens46; specifcally, we used disposable gloves and dis- infected with bleach equipment and boots before changing location. During each month, all surveys were performed within 4 days to limit the variation of environmental conditions which may alter the local ecological opportunity38,41. To limit pseudoreplication, each site was surveyed only once per month. On the other hand, the

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two surveys performed on the same populations during diferent months (July and September) create the condition to test additional hypotheses, like the assessment of temporal variability on salamanders’ trophic niche41, or even to employ specifc sofware to individually recognize salamanders47,48 and focus future research on single individuals as well. A blinded stomach contents analysis was performed to limit possible bias49. Te methodology used to shoot photos enables the production of standardised high quality images with low impact on the species23,50. Te white calibration before each session avoided potential divergence in light condition and thus, providing standardised pictures. Tis method allows the creation of a “living” digital collection of Speleomantes23, a method that does not only avoid the sacrifce of animals (and thus the related stochastic efects conditioning the evolution of populations) but it is also repeatable. For future updates we will try to include an estimation of prey volume, preferably using methodologies allowing to directly measure the volume (i.e., immerging residuals in a liquid). Usage Notes Te frst part of this dataset is composed of high quality images of individuals of Speleomantes strinatii and S. ambrosii from dorsal view. We suggest the use of the program ImageJ to extrapolate salamanders morphometrics and to estimate their snout-vent length35, a fundamental parameter to distinguish juveniles from adults1. Te images can be also used in R environment (http://www.R-project.org/) to perform analysis on multiple phenotypic traits51,52. Considering that the dorsal pattern of Speleomantes does not change through the time33, it can be used as natural mark to individually recognize salamanders48,53. Te repeated surveys performed on the same locations were thought to test the efcacy of specifc sofware to automatically recognize Speleomantes salaman- ders47,53. It has been observed that the ventral pattern of the mainland S. strinatii can be used to individually recognize salamanders, and that sofware can be employed to do it automatically54. However, only the three mainland species have visible pigments on their ventral side1, thus alternative methods are needed for analyses embrac- ing all Speleomantes (i.e. including the fve Sardinian species). Furthermore, the recognition of Speleomantes using their dorsal pattern limits individuals’ handling, a potential source of both stress and pathogens50,55–57. Te population S_strinatii2 in the previously published dataset23 included two nearby sites, the S_strinatii2 and S_strinatii5 shown here. To combine these data, the actual S_strinatii2 contains the previous 20 photos (1045074–1045113) and the actual S_strinatii5 the other 20 (1045114–1045142). Furthermore, also the population S_strinatii3 in the previous dataset23 includes 2 diferent nearby sites; we therefore suggest to split it in S_strinatii3 with the frst 33 photos (1045032–1045056) and in S_strinatii7 with the other 10 (1045057–1045069). Te second part of the dataset is provided in CSV format and is ready to be analysed with R. Populations are codifed following23. To combine the data on stomach contents with the previous dataset:9 S_ambrosii2 = Cave_ ambrosii1, S_ambrosii3 = Cave_ambrosii3, S_ambrosii4 = Cave_ambrosii2. Te data on stomach contents allows to assess diferent characteristics of the populations trophic niche34,38,41. However, only afer verifying whether the single individuals were captured during the two diferent surveys, it is possible to evaluate the variation of individuals’ trophic niche over time. Code availability No code was used in this study.

Received: 25 January 2021; Accepted: 27 April 2021; Published: xx xx xxxx

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